Big data analysis of a mini three-axis CNC machine tool based on the tuning operation of controller parameters

Abstract

The characteristic responses of a mini three-axis computer numerical control (CNC) machine tool based on the controller tuning operation were investigated for big data estimation. The major tuning parameters included the position control gains K _p, the position feed-forward control gains K _f, the speed control gains K _v, and the gain ratios K _g of the position and speed control values in manufacturing industries. K _p gains of 10, 30, 50, 80, 100, 200, 300, and 400 rad/s, K _f gains of 0, 30, 50, 60, 80, and 100 %, K _v gains of 30, 50, 70, 100, 300, 900, 2000, and 3000 rad/s, and K _g ratios of (1:1), (3:1), (5:1), and (7:1) were analyzed for smart productivity. The results show that the settling times at different K _p values were almost constant when the K _p gain was over 200 rad/s. The maximum overshoots, when the feed-forward gain is over 60 %, almost increased with increasing feed-forward gains. However, the overshoot of the three-axis CNC machine tool decreased as the K _v gain increased until the K _v gain reached 70 rad/s. The settling times at a constant K _g ratio decreased with an increase in the K _p and K _v gains. The characteristic responses of the tuning operations were enabled with connectivity to a cloud network to share the big data, to support decision making, and to adjust operations in real time.